A multi-resolution approach to learning with overlapping communities

The recent few years have witnessed a rapid surge of par-

ticipatory web and social media, enabling a new laboratory

for studying human relations and collective behavior on an

unprecedented scale. In this work, we attempt to harness

the predictive power of social connections to determine the

preferences or behaviors of individuals such as whether a

user supports a certain political view, whether one likes one

product, whether he/she would like to vote for a presidential

candidate, etc. Since an actor is likely to participate in mul-

tiple dierent communities with each regulating the actor's

behavior in varying degrees, and a natural hierarchy might

exist between these communities, we propose to zoom into

a network at multiple dierent resolutions and determine

which communities are informative of a targeted behavior.

We develop an ecient algorithm to extract a hierarchy of

overlapping communities. Empirical results on several large-

scale social media networks demonstrate the superiority of

our proposed approach over existing ones without consider-

ing the multi-resolution or overlapping property, indicating

its highly promising potential in real-world applications